




import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard

@pytest.mark.parametrize(
    'compression_format, is_archive'     ,     [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]     ,     )
def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      Optional[int]     ,     __UpperCAmelCase		:      Any     ,     __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      Tuple     ,     )   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     {
          '7z': (seven_zip_file, SevenZipExtractor),
          'bz2': (bza_file, BzipaExtractor),
          'gzip': (gz_file, GzipExtractor),
          'lz4': (lza_file, LzaExtractor),
          'tar': (tar_file, TarExtractor),
          'xz': (xz_file, XzExtractor),
          'zip': (zip_file, ZipExtractor),
          'zstd': (zstd_file, ZstdExtractor),
      }
      SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     input_paths_and_base_extractors[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE_      		=     f"for '{compression_format}' compression_format, "
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(__UpperCAmelCase				)
      assert base_extractor.is_extractable(__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     tmp_path / ('extracted' if is_archive else 'extracted.txt')
      base_extractor.extract(__UpperCAmelCase     ,     __UpperCAmelCase				)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE_      		=     file_path.read_text(encoding='utf-8'				)
      else:
            SCREAMING_SNAKE_CASE_      		=     output_path.read_text(encoding='utf-8'				)
      SCREAMING_SNAKE_CASE_      		=     text_file.read_text(encoding='utf-8'				)
      assert extracted_file_content == expected_file_content

@pytest.mark.parametrize(
    'compression_format, is_archive'     ,     [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]     ,     )
def    UpperCAmelCase_				( __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Any     ,     __UpperCAmelCase		:      Optional[int]     ,     __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      List[str]     ,     )   ->					Optional[int]:
      SCREAMING_SNAKE_CASE_      		=     {
          '7z': seven_zip_file,
          'bz2': bza_file,
          'gzip': gz_file,
          'lz4': lza_file,
          'tar': tar_file,
          'xz': xz_file,
          'zip': zip_file,
          'zstd': zstd_file,
      }
      SCREAMING_SNAKE_CASE_      		=     input_paths[compression_format]
      if input_path is None:
            SCREAMING_SNAKE_CASE_      		=     f"for '{compression_format}' compression_format, "
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     Extractor.infer_extractor_format(__UpperCAmelCase				)
      assert extractor_format is not None
      SCREAMING_SNAKE_CASE_      		=     tmp_path / ('extracted' if is_archive else 'extracted.txt')
      Extractor.extract(__UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase				)
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  SCREAMING_SNAKE_CASE_      		=     file_path.read_text(encoding='utf-8'				)
      else:
            SCREAMING_SNAKE_CASE_      		=     output_path.read_text(encoding='utf-8'				)
      SCREAMING_SNAKE_CASE_      		=     text_file.read_text(encoding='utf-8'				)
      assert extracted_file_content == expected_file_content

@pytest.fixture
def    UpperCAmelCase_				( __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      int				)   ->					int:
      import tarfile

      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'data_dot_dot'
      directory.mkdir()
      SCREAMING_SNAKE_CASE_      		=     directory / 'tar_file_with_dot_dot.tar'
      with tarfile.TarFile(__UpperCAmelCase     ,     'w'				) as f:
            f.add(__UpperCAmelCase     ,     arcname=os.path.join('..'     ,     text_file.name				)				)
      return path

@pytest.fixture
def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]				)   ->					str:
      import tarfile

      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'data_sym_link'
      directory.mkdir()
      SCREAMING_SNAKE_CASE_      		=     directory / 'tar_file_with_sym_link.tar'
      os.symlink('..'     ,     directory / 'subdir'     ,     target_is_directory=__UpperCAmelCase				)
      with tarfile.TarFile(__UpperCAmelCase     ,     'w'				) as f:
            f.add(str(directory / 'subdir'				)     ,     arcname='subdir'				)  # str required by os.readlink on Windows and Python < 3.8
      return path

@pytest.mark.parametrize(
    'insecure_tar_file, error_log'     ,     [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')]     ,     )
def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Optional[Any]				)   ->					Dict:
      SCREAMING_SNAKE_CASE_      		=     {
          'tar_file_with_dot_dot': tar_file_with_dot_dot,
          'tar_file_with_sym_link': tar_file_with_sym_link,
      }
      SCREAMING_SNAKE_CASE_      		=     insecure_tar_files[insecure_tar_file]
      SCREAMING_SNAKE_CASE_      		=     tmp_path / 'extracted'
      TarExtractor.extract(__UpperCAmelCase     ,     __UpperCAmelCase				)
      assert caplog.text
      for record in caplog.records:
            assert record.levelname == "ERROR"
            assert error_log in record.msg





def    UpperCAmelCase_				( __UpperCAmelCase		:      List[str]				)   ->					int:
      # We should have less false positives than zipfile.is_zipfile
      # We do that by checking only the magic number
      SCREAMING_SNAKE_CASE_      		=     tmpdir / 'not_a_zip_file'
      # From: https://github.com/python/cpython/pull/5053
      SCREAMING_SNAKE_CASE_      		=     (
          b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
          b'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
          b'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
          b'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
      )
      with not_a_zip_file.open('wb'				) as f:
            f.write(__UpperCAmelCase				)
      assert zipfile.is_zipfile(str(__UpperCAmelCase				)				)  # is a false positive for `zipfile`
      assert not ZipExtractor.is_extractable(__UpperCAmelCase				)  # but we're right



'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_						:  List[str]				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[Any]				  =						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase_						:  Tuple				  =						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				for attribute in key.split("""."""	):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				if weight_type is not None:
								_SCREAMING_SNAKE_CASE					:			int							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	).shape
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
								    f""" {value.shape} for {full_name}"""	)

				if weight_type == "weight":
								_SCREAMING_SNAKE_CASE					:			int							= value
				elif weight_type == "weight_g":
								_SCREAMING_SNAKE_CASE					:			List[str]							= value
				elif weight_type == "weight_v":
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= value
				elif weight_type == "bias":
								_SCREAMING_SNAKE_CASE					:			Tuple							= value
				elif weight_type == "running_mean":
								_SCREAMING_SNAKE_CASE					:			Tuple							= value
				elif weight_type == "running_var":
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value
				elif weight_type == "num_batches_tracked":
								_SCREAMING_SNAKE_CASE					:			Any							= value
				elif weight_type == "inv_freq":
								_SCREAMING_SNAKE_CASE					:			List[str]							= value
				else:
								_SCREAMING_SNAKE_CASE					:			List[Any]							= value

				logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= fairseq_model.state_dict()

				_SCREAMING_SNAKE_CASE					:			Any							= hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= False
								if "conv_layers" in name:
												load_conv_layer(
												    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, hf_model.config.feat_extract_norm == """group"""				, )
												_SCREAMING_SNAKE_CASE					:			str							= True
								else:
												for key, mapped_key in MAPPING.items():
																_SCREAMING_SNAKE_CASE					:			List[Any]							= """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
																				_SCREAMING_SNAKE_CASE					:			List[str]							= True
																				if "*" in mapped_key:
																								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= name.split(SCREAMING_SNAKE_CASE__	)[0].split("""."""	)[-2]
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= mapped_key.replace("""*"""				, SCREAMING_SNAKE_CASE__	)
																				if "pos_bias_u" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= None
																				elif "pos_bias_v" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[int]							= None
																				elif "weight_g" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """weight_g"""
																				elif "weight_v" in name:
																								_SCREAMING_SNAKE_CASE					:			Any							= """weight_v"""
																				elif "bias" in name:
																								_SCREAMING_SNAKE_CASE					:			Dict							= """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_SCREAMING_SNAKE_CASE					:			int							= """weight"""
																				elif "running_mean" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """running_mean"""
																				elif "inv_freq" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """inv_freq"""
																				elif "running_var" in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """running_var"""
																				elif "num_batches_tracked" in name:
																								_SCREAMING_SNAKE_CASE					:			Tuple							= """num_batches_tracked"""
																				else:
																								_SCREAMING_SNAKE_CASE					:			Dict							= None
																				set_recursively(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
																continue
								if not is_used:
												unused_weights.append(SCREAMING_SNAKE_CASE__	)

				logger.warning(f"""Unused weights: {unused_weights}"""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= full_name.split("""conv_layers."""	)[-1]
				_SCREAMING_SNAKE_CASE					:			Tuple							= name.split("""."""	)
				_SCREAMING_SNAKE_CASE					:			Any							= int(items[0]	)
				_SCREAMING_SNAKE_CASE					:			str							= int(items[1]	)

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			Tuple							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			List[Any]							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    f"""{full_name} has size {value.shape}, but"""
																    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
												_SCREAMING_SNAKE_CASE					:			str							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
				else:
								unused_weights.append(SCREAMING_SNAKE_CASE__	)







@torch.no_grad()
def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=True	):







				"""simple docstring"""





				if config_path is not None:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= WavaVecaConformerConfig.from_pretrained(SCREAMING_SNAKE_CASE__				, hidden_act="""swish"""	)
				else:
								_SCREAMING_SNAKE_CASE					:			List[str]							= WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								_SCREAMING_SNAKE_CASE					:			List[str]							= """rotary"""

				if is_finetuned:
								if dict_path:
												_SCREAMING_SNAKE_CASE					:			List[str]							= Dictionary.load(SCREAMING_SNAKE_CASE__	)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= target_dict.pad_index
												_SCREAMING_SNAKE_CASE					:			int							= target_dict.bos_index
												_SCREAMING_SNAKE_CASE					:			Any							= target_dict.eos_index
												_SCREAMING_SNAKE_CASE					:			List[str]							= len(target_dict.symbols	)
												_SCREAMING_SNAKE_CASE					:			Dict							= os.path.join(SCREAMING_SNAKE_CASE__				, """vocab.json"""	)
												if not os.path.isdir(SCREAMING_SNAKE_CASE__	):
																logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__	)	)
																return
												os.makedirs(SCREAMING_SNAKE_CASE__				, exist_ok=SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Dict							= target_dict.indices

												# fairseq has the <pad> and <s> switched
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 0
												_SCREAMING_SNAKE_CASE					:			List[Any]							= 1
												with open(SCREAMING_SNAKE_CASE__				, """w"""				, encoding="""utf-8"""	) as vocab_handle:
																json.dump(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
												_SCREAMING_SNAKE_CASE					:			Tuple							= WavaVecaCTCTokenizer(
												    SCREAMING_SNAKE_CASE__				, unk_token=target_dict.unk_word				, pad_token=target_dict.pad_word				, bos_token=target_dict.bos_word				, eos_token=target_dict.eos_word				, word_delimiter_token="""|"""				, do_lower_case=SCREAMING_SNAKE_CASE__				, )
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= True if config.feat_extract_norm == """layer""" else False
												_SCREAMING_SNAKE_CASE					:			Dict							= WavaVecaFeatureExtractor(
												    feature_size=1				, sampling_rate=1_6000				, padding_value=0				, do_normalize=SCREAMING_SNAKE_CASE__				, return_attention_mask=SCREAMING_SNAKE_CASE__				, )
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				, tokenizer=SCREAMING_SNAKE_CASE__	)
												processor.save_pretrained(SCREAMING_SNAKE_CASE__	)

								_SCREAMING_SNAKE_CASE					:			str							= WavaVecaConformerForCTC(SCREAMING_SNAKE_CASE__	)
				else:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaConformerForPreTraining(SCREAMING_SNAKE_CASE__	)

				if is_finetuned:
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]				, arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
				else:
								_SCREAMING_SNAKE_CASE					:			Tuple							= argparse.Namespace(task="""audio_pretraining"""	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__	)

								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				, task=SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			Optional[int]							= model[0].eval()

				recursively_load_weights(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, not is_finetuned	)

				hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				UpperCAmelCase_						:  List[Any]				  =						parser.parse_args()
				convert_wavaveca_conformer_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)




